Gene Risk Scores Work Unevenly Across Populations, Study Warns
Researchers analyzing data from nine global biobanks found that genetic risk prediction tools perform inconsistently depending on ancestry and disease type—with European ancestry panels sometimes outperforming locally optimized ones. The finding suggests precision medicine initiatives need standardized best practices to avoid widening health equity gaps.
Originaltitel: Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
<p>Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.</p>